Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	144,963
2	Rhode Island	143,740
3	South Dakota	140,569
4	Utah	127,823
5	Tennessee	124,793
6	Arizona	121,882
7	Iowa	118,111
8	Wisconsin	116,186
9	Nebraska	115,718
10	South Carolina	115,601
11	Oklahoma	114,957
12	New Jersey	114,813
13	Arkansas	114,012
14	Delaware	112,325
15	Indiana	111,831
16	Alabama	111,738
17	Illinois	109,833
18	Kansas	109,021
19	Idaho	108,457
20	New York	108,356
21	Mississippi	107,313
22	Florida	107,124
23	Minnesota	107,093
24	Nevada	106,351
25	Wyoming	105,803
26	Montana	105,689
27	Georgia	104,019
28	Kentucky	103,752
29	Massachusetts	102,855
30	Texas	102,510
31	Louisiana	102,372
32	Missouri	101,385
33	Michigan	99,714
34	Connecticut	97,738
35	New Mexico	97,419
36	North Carolina	96,264
37	California	96,257
38	Colorado	96,250
39	Alaska	95,868
40	Pennsylvania	94,778
41	Ohio	94,722
42	West Virginia	91,032
43	Virginia	79,410
44	Maryland	76,342
45	New Hampshire	72,914
46	District of Columbia	69,683
47	Washington	58,671
48	Puerto Rico	54,181
49	Maine	51,035
50	Oregon	48,640
51	Vermont	38,992
52	Hawaii	25,262

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Colorado	78
2	Utah	74
3	Wyoming	69
4	Missouri	65
5	Arizona	60
6	Washington	57
7	Arkansas	50
8	Nevada	47
9	Indiana	43
10	Idaho	42
11	Oklahoma	41
12	Hawaii	40
13	Louisiana	39
14	Oregon	39
15	West Virginia	38
16	Alabama	37
17	North Carolina	37
18	New Mexico	36
19	Kentucky	32
20	Alaska	30
21	Mississippi	29
22	Delaware	28
23	Maine	28
24	Montana	28
25	Michigan	27
26	Kansas	25
27	New Jersey	25
28	South Carolina	25
29	Georgia	24
30	Minnesota	24
31	Texas	24
32	North Dakota	23
33	Ohio	23
34	Iowa	21
35	Pennsylvania	21
36	New York	20
37	California	19
38	New Hampshire	18
39	Rhode Island	18
40	Illinois	17
41	Puerto Rico	17
42	Maryland	12
43	Virginia	11
44	Wisconsin	11
45	Connecticut	10
46	Massachusetts	10
47	District of Columbia	9
48	Nebraska	9
49	Vermont	5
50	South Dakota	4
51	Tennessee	4
52	Florida	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,964
2	New York	2,723
3	Massachusetts	2,603
4	Rhode Island	2,569
5	Mississippi	2,470
6	Arizona	2,441
7	Connecticut	2,317
8	Alabama	2,299
9	Louisiana	2,291
10	South Dakota	2,290
11	Pennsylvania	2,151
12	Michigan	2,080
13	New Mexico	2,052
14	Indiana	2,041
15	North Dakota	2,035
16	Illinois	2,011
17	Arkansas	1,942
18	Iowa	1,934
19	Georgia	1,928
20	South Carolina	1,901
21	Oklahoma	1,851
22	Nevada	1,826
23	Tennessee	1,815
24	Texas	1,797
25	Kansas	1,770
26	Florida	1,735
27	Delaware	1,724
28	Ohio	1,718
29	Kentucky	1,613
30	District of Columbia	1,612
31	Maryland	1,602
32	California	1,598
33	Missouri	1,597
34	West Virginia	1,591
35	Montana	1,537
36	Wisconsin	1,376
37	Minnesota	1,349
38	Virginia	1,325
39	Nebraska	1,301
40	North Carolina	1,269
41	Wyoming	1,252
42	Colorado	1,186
43	Idaho	1,185
44	New Hampshire	1,002
45	Puerto Rico	792
46	Washington	764
47	Utah	724
48	Oregon	654
49	Maine	628
50	Alaska	486
51	Vermont	410
52	Hawaii	355

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Kansas	2
2	Michigan	2
3	Arizona	1
4	Indiana	1
5	Kentucky	1
6	Minnesota	1
7	Virginia	1
8	Alabama	0
9	Alaska	0
10	Arkansas	0
11	California	0
12	Colorado	0
13	Connecticut	0
14	Delaware	0
15	District of Columbia	0
16	Florida	0
17	Georgia	0
18	Hawaii	0
19	Idaho	0
20	Illinois	0
21	Iowa	0
22	Louisiana	0
23	Maine	0
24	Maryland	0
25	Massachusetts	0
26	Mississippi	0
27	Missouri	0
28	Montana	0
29	Nebraska	0
30	Nevada	0
31	New Hampshire	0
32	New Jersey	0
33	New Mexico	0
34	New York	0
35	North Carolina	0
36	North Dakota	0
37	Ohio	0
38	Oklahoma	0
39	Oregon	0
40	Pennsylvania	0
41	Puerto Rico	0
42	Rhode Island	0
43	South Carolina	0
44	South Dakota	0
45	Tennessee	0
46	Texas	0
47	Utah	0
48	Vermont	0
49	West Virginia	0
50	Wisconsin	0
51	Wyoming	0
52	Washington	-1

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	404,328	1	99
Crowley	Colorado	364,626	2	99
Bent	Colorado	277,031	3	99
Dewey	South Dakota	250,849	4	99
Lincoln	Arkansas	247,236	5	99
Davidson	Tennessee	143,338	194	93
Richland	South Carolina	113,922	998	68
York	South Carolina	113,749	1004	68
Orange	California	85,885	2269	27
Pierce	Washington	62,218	2830	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,350	2	99
Hancock	Georgia	8,159	3	99
Jerauld	South Dakota	7,948	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,607	1855	40
York	South Carolina	1,392	2110	32
Richland	South Carolina	1,383	2122	32
Davidson	Tennessee	1,383	2123	32
Pierce	Washington	695	2807	10

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.